Home Page




Editorial Board


Open Source Software




Frequently Asked Questions

Contact Us

RSS Feed

Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines

Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin; 9(45):1369−1398, 2008.


Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification and natural language processing. In this paper, we propose a novel coordinate descent algorithm for training linear SVM with the L2-loss function. At each step, the proposed method minimizes a one-variable sub-problem while fixing other variables. The sub-problem is solved by Newton steps with the line search technique. The procedure globally converges at the linear rate. As each sub-problem involves only values of a corresponding feature, the proposed approach is suitable when accessing a feature is more convenient than accessing an instance. Experiments show that our method is more efficient and stable than state of the art methods such as Pegasos and TRON.

© JMLR 2008. (edit, beta)